A scene viewed by a hyperspectral imaging system is often displayed as a three-dimensional datacube, where two dimensions represent the spatial domain (x, y) and one dimension represents the spectral domain (λ). Spatial observations (x, y) allow a person to observe an image when high contrast is available. However, during conditions of low contrast, such as fog, smoke, camouflage, and/or darkness, spectral signatures help identify otherwise unobservable objects. Hyperspectral imagers are capable of collecting and processing objects within a scene by dividing the whole spectrum into tens or even hundreds of bands and thus can obtain high resolution datacubes useful in a wide range of applications such as mining, agriculture, chemical detection, and military surveillance. Conventional hyperspectral devices face a tradeoff between spectral resolution and the signal to noise ratio (SNR) of the estimated spectrum. Thus, there is an ongoing need for imaging systems that maximize the signal to noise ratio (SNR) of the estimated spectrum without sacrificing capability of generating datacubes with high spectral resolution.